Abstract
Safety assessment of road sections and networks have historically relied on police-reported crash data. These data have several noteworthy and significant shortcomings, including under-reporting, subjectivism, post hoc assessment of crash causes and contributing factors, limited behavioural information, and omitted potential important crash-related factors resulting in an omitted variable bias. Moreover, crashes are relatively rare events and require long observation periods to justify expenditures. The rarity of crashes leads to a moral dilemma—we must wait for sufficient crashes to accrue at a site—some involving injuries and even death—to then justify improvements to prevent crashes. The more quickly the profession can end its reliance on crashes to assess road safety, the better.Surrogate safety assessment methodologies, in contrast, are proactive in design, do not rely on crashes, and require shorter observation timeframes in which to formulate reliable safety assessments. Although surrogate safety assessment methodologies have been developed and assessed over the past 50 years, an overarching and unifying framework does not exist to date. A unifying framework will help to contextualize the role of various methodological developments and begin a productive discussion in the literature about how the various pieces do or should fit together to understand road user risk better.This paper aims to fill this gap by thoroughly mapping traffic conflicts and surrogate safety methodologies. A total of 549 studies were meticulously reviewed to achieve this aim of developing a unifying framework. The resulting framework provides a consolidated and up-to-date summary of surrogate safety assessment methodologies and conflict measures and metrics.Further work is needed to advance surrogate safety methodologies. Critical research needs to include identifying a comprehensive and reliable set of surrogate measures for risk assessment, establishing rigorous relationships between conflicts and crashes, developing ways to capture road user behaviours into surrogate-based safety assessment, and integrating crash severity measures into risk estimation.
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